TP20. TP020 TELEHEALTH AND REMOTE MONITORING FOR PULMONARY, CRITICAL CARE, AND SLEEP最新文献

{"title":"Patient-Reported Symptom Severity and Pulse Oximetry in the Covid-19 Remote Monitoring Programme in Ireland","authors":"C. Edwards, E. Costello, M. Curley, L. Smyth, C. O’Seaghdha, R. Costello, K. O'Reilly","doi":"10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a1728","DOIUrl":"https://doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a1728","url":null,"abstract":"Rationale: A total of 60,287 (1,267/100,000) cases of Covid-19 (SARS-CoV-2) were recorded in Ireland by 30 October 2020. An important strategy to free up in-hospital capacity was development of a remote monitoring platform to support at-home care or early discharge of lower-risk patients with mild/moderate Covid-19 symptoms. Methods: The monitoring platform consisted of a patient-facing app + pulse oximeter (Bluetoothconnected Nonin 3230) enabling patients to record symptoms (e.g. breathlessness, diarrhea;severity rated on a 10-point scale), temperature & oxygen saturation (SpO2). Patients were prompted to record measurement 4 times/day. Patient-recorded data was viewed in real time by their healthcare centre via a dedicated web-based monitoring portal. Criteria for remote monitoring included: Covid-19 symptoms, positive for SARS-CoV-2, young age, absence of serious concomitant conditions, need for continued observation post-discharge. Treatment centres emailed app installation instructions and supplied a pulse oximeter to their patients. Treatment centres & patients received alerts if pulse oximetry values crossed pre-defined thresholds. Results: Between 13 March and 31 October 2020, 1,045 patients at 8 primary & 15 secondary care centres had used the remote monitoring platform [median duration: 13 days (interquartile range 10-23 days)]. 11 patients were admitted to hospital and 12 previously hospitalized patients were readmitted. 933 patients (89%) gave consent to use of their pseudonymised data for research. Symptoms and physiological markers of severity of infection varied considerably. 871 patients recorded breathlessness data with 53 rating severity as 6/10 and 23 as 8/10. 300 patients recorded diarrhea data with 24 rating severity as 6/10 and 6 as 8/10 (see Figure). SpO2 data were available for 907 patients. 733 patients reported SpO2 94-96%, 334 reported SpO2 92-93%and 265 patients reported SpO2 ≤91% at least once during the monitoring period. Conclusions: Remote monitoring of Covid-19 in appropriate patients can free up in-hospital capacity. The majority of these patients were willing to provide pseudonymised data to support research on Covid-19. .","PeriodicalId":159700,"journal":{"name":"TP20. TP020 TELEHEALTH AND REMOTE MONITORING FOR PULMONARY, CRITICAL CARE, AND SLEEP","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121080076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Virtual Care Model Utilizing Patient Directed Oximetry Monitoring for Outpatients with COVID-19: A Quality Improvement Study","authors":"M. Devlin, M. Nicholson, J. Ernst, I. Dhaliwal, M. Mrkobrada, E. Spicer","doi":"10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a1731","DOIUrl":"https://doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a1731","url":null,"abstract":"Background: There is a care gap for outpatients with COVID-19, with many lacking access to standardized medical care. We combined virtual clinical assessment with patient-directed oximetry to enhance clinical care of these patients. The aim of this study was to assess the role of oximetry and clinical outcomes of those enrolled in this novel clinical initiative. Methods: A team of General Internal Medicine, Infectious Diseases, and Respirology physicians in London, Ontario, partnered with the local public health unit (Middlesex London Health Unit) to enroll outpatients diagnosed with COVID-19. We assessed patients virtually and arranged for the same day delivery of an oximetry device to the patient's home in order to assess for hypoxemia. In this quality improvement study, we present our initial experience with the use of oximeters in virtual care and 30-day patient outcomes utilizing this novel clinical model. Results: Between April 23-May 19th, 2020, we assessed and monitored 51 patients in the community with COVID-19. Of these, 47% had an oximeter delivered to their residence. A majority of patients (91%) who experienced severe dyspnea had normal oxygen saturations. Our clinical intervention resulted in 3 direct admissions to a designated COVID-19 unit at a local hospital for decompensating patients. No deaths were noted. We have characterized a number of significant outcomes that warrant further medical and allied health follow up. Interpretation: We present a clinical model that supports the care and symptomatic management of patients in the community with COVID-19. Oximetry was found to primarily exclude the presence of hypoxemia in dyspneic patients, while identifying few patients with true hypoxemia. .","PeriodicalId":159700,"journal":{"name":"TP20. TP020 TELEHEALTH AND REMOTE MONITORING FOR PULMONARY, CRITICAL CARE, AND SLEEP","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124444266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of Telehealth Interventions on Improving Quality of Life in High Risk VA Patients","authors":"M. Saeed, C. Yen, I. Adrian, B. Allison, N. Chaisson, M. Korsten, J. Post, M. Radulovic","doi":"10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a1717","DOIUrl":"https://doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a1717","url":null,"abstract":"","PeriodicalId":159700,"journal":{"name":"TP20. TP020 TELEHEALTH AND REMOTE MONITORING FOR PULMONARY, CRITICAL CARE, AND SLEEP","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115466985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of a Home Monitoring System for COPD Patients During the SARS-CoV-2 Pandemic: A Feasibility Study","authors":"M. Rydberg, A. McDaniel-Harper, K. Hardy, P. Burkett, E. Johnson, M. B. Drummond","doi":"10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a1732","DOIUrl":"https://doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a1732","url":null,"abstract":"RATIONALE: Remote monitoring of COPD patients has the potential to improve clinical outcomes. The ability to successfully deploy home monitoring technologies to COPD patients remotely without in-person encounters is of particular interest during the SARS-CoV-2 pandemic. We present interim results from a prospective implementation study of a home monitoring system in COPD patients at-risk for frequent acute exacerbations of COPD (AECOPD). METHODS: We recruited non-hospitalized individuals aged 40-80 years with spirometryconfirmed COPD and increased AECOPD risk (one hospitalization or two outpatient AECOPD in the prior year). The home system includes: a GoHome™ Data Collection Platform and GoSpiro® spirometer (Monitored Therapeutics, Dublin, OH), and a 3230 pulse oximeter (Nonin Medical, Plymouth, MN). The tablet-based GoHome™ has an auto-start system requiring no computer skills for operation. Eligible participants were contacted via phone, and if interested, were sent a participation kit containing informed consent and the home system. After remotely collecting ICF, participants completed device setup and baseline spirometry using Avatar coaching. At set times, the device collects responses for an automated COPD Action Plan and displays reminders for the patient to use the integrated Bluetooth® spirometer and pulse oximeter. The GoSpiro® measures slow vital capacity (SVC) and forced vital capacity (FVC) using an Avatar-assisted technology coach on the GoHome™ (Figure). The Avatar coaches the patient through each measurement following ATS recommendations for instructions and coaching, followed by error identification and maneuver error correction without human intervention. Patients are engaged daily with the COPD Action Plan. Automated scores return immediate patient guidance along with appropriate clinician alerts. Results are cellular or Wi-Fi uploaded to a cloud server for realtime investigator review. Following demonstrated proficiency, daily measurements of spirometry (FVC Tuesday/Thursday, SVC all other days), daily pulse oximetry and COPD Action Plan were performed. Participant study duration was three months. RESULTS: To date, seven of 12 planned participants have been enrolled. All enrolled participants have successfully activated all device components and performed FVC maneuvers meeting ATS acceptability standards. All participants were able to complete collection and transmission of daily pulse oximetry and COPD Action Plan data. One participant requested study withdrawal after three weeks and six participants remain on study. CONCLUSIONS: Deployment of a COPD home telemonitoring system platform including daily spirometry, pulse oximetry and electronic questionnaire without in-person contact is feasible. This technology may be useful in settings where in-person visits are not feasible due to patient safety, remote location or access-related issues. .","PeriodicalId":159700,"journal":{"name":"TP20. TP020 TELEHEALTH AND REMOTE MONITORING FOR PULMONARY, CRITICAL CARE, AND SLEEP","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133880171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Compliance Analysis for Remote Spirometry in Subjects with Mild to Moderate Asthma","authors":"C. Huang, P. Kelly, M. K. Ruddy, E. Izmailova, R. Ellis","doi":"10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a1733","DOIUrl":"https://doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a1733","url":null,"abstract":"RATIONALE Remote spirometry measures performed by means of handheld spirometers connected to smartphone applications have gained increased attention as a convenient data collection technique for patients. Additionally, this method provides more frequent data than clinical visits and allows researchers to control for data variability (diurnal variation, seasonal/environmental changes) by increasing statistical degrees of freedom. Moreover, remote and clinic spirometry data was shown to be comparable in patients with asthma and COPD. The need for remote assessments became more acute during the COVID-19 pandemic when healthcare professionals were urged to keep clinic visits to an absolute minimum to minimize the risk of infection to patients. However, a concern about remote spirometry modality is related to patient compliance remains valid: will patients perform spirometry maneuvers remotely while unsupervised? METHODS We analyzed remote spirometry compliance data from 2 clinical trials of patients with mild to moderate asthma conducted in the US and 1 in the UK. The former were single centers studies of 28-day duration and recruited 32 subjects. The latter was a multicenter study which recruited 39 subjects for 5-6 months;however only the first 28-day treatment data was analyzed to match the timeframe of the other studies. The study subjects received both experimental and standard of care treatments. All study subjects were instructed to perform pulmonary function tests at home twice daily, received a handheld spirometer, a dedicated smart phone along with training how to perform spirometry maneuvers remotely. All studies deployed mobile spirometer devices that synchronize with the smartphone application. Patients were asked to contribute spirometry data at predefined time windows (morning and evening). Compliance rates were calculated as a percentage of pulmonary function tests comprising a minimum of 2 complete maneuvers twice daily versus twice days on study. RESULTS The twice daily data compliance across three studies was 88.9%, the compliance for performing spirometry maneuvers within the specified time windows was 85% and compliance for no missing day during the study period was 83%. Additionally, we analyzed the number of compliant subjects over time across all studies: the number of compliant subjects did not decline over the period of 28 days. Moreover, the compliance analysis stratified by the time of the day and weekday/weekend demonstrated no difference in compliance. CONCLUSIONS Our results demonstrate good compliance for remote spirometry data collection for 28-day period indicating that remote spirometry data collection is feasible in the multi-center clinical trials recruiting asthma patients. .","PeriodicalId":159700,"journal":{"name":"TP20. TP020 TELEHEALTH AND REMOTE MONITORING FOR PULMONARY, CRITICAL CARE, AND SLEEP","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128363738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Post COVID-19 Remote Patient Monitoring Following Discharge from NYC Hospital","authors":"D. Copeland, E. Eisenberg, C. Edwards, N. Shah, C. Powell","doi":"10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a1727","DOIUrl":"https://doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a1727","url":null,"abstract":"RATIONALE: Patients discharged after hospitalization for COVID-19 pneumonia are at high risk for readmission and mortality. Early in the pandemic we noted that many patients discharged after initial improvement of their COVID pneumonia were subsequently readmitted with progressive hypoxemic respiratory failure. Therefore, we implemented a remote patient monitoring program to track pulse oximetry, heart rate and dyspnea after COVID- 19 hospitalization. The goal was twofold: to optimize hospital utilization and resources by expeditiously discharging stable patients and to improve patient safety after discharge with continued close monitoring at home. METHODS: Patients were eligible for 90-day remote monitoring if they were being discharged home, could access a smart phone and required supplemental oxygen during hospitalization. Enrolled patients received a Bluetooth enabled Nonin 3230 pulse oximeter and installed a mobile application provided by patientMpower, Ltd. for input of dyspnea symptoms. Patients were prompted to check oxygenation and input symptoms twice daily. Recorded data was transmitted to a monitoring portal;abnormal recordings triggered an alert;all data was reviewed by an APP (Advanced Practice Provider) and patients with alerts were contacted. Responses to alerts included change in medication regimen, adjustment of oxygen delivery, expedited follow-up visit scheduling, and emergency room referral. Remote monitoring data were reviewed at the scheduled post-discharge pulmonologist appointment. RESULTS: Between 4/28/20 and 11/30/20, 111 patients at Mount Sinai Hospital were enrolled in the remote monitoring program with 87 (78%) participants providing at least one entry. The mean age was 60 years (SD ± 14) and 59% were male. The median device usage was 84 days with 64% of patients reporting an oxygen saturation ≤ 91% during monitoring. 53% of patients reported at least one instance of dyspnea. There were on average 46.4 alerts per month with the majority stemming from oxygen saturations <95% and 49 outreach attempts a month. Table 1 summarizes these data. CONCLUSIONS: We describe the successful implementation of a remote monitoring program at a tertiary care center in NYC during the COVID-19 pandemic. Our subjective experience is that the ability to remotely monitor patients increased provider comfort when expediting discharges of medically stable patients. The program alerts reflected periods of worsening pulmonary status and triggered interactions that provided more continuous contact between providers and patients. Our next steps are to leverage the data from prolonged monitoring to gain insights into the recovery of COVID-19 patients and to determine factors associated with post discharge readmissions and mortality. .","PeriodicalId":159700,"journal":{"name":"TP20. TP020 TELEHEALTH AND REMOTE MONITORING FOR PULMONARY, CRITICAL CARE, AND SLEEP","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130000776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accuracy of the Albus Home Research Device (RD) for the Non-Contact and Passive Monitoring of Nocturnal Respiratory Rate at Home in an Adult Population","authors":"W. Do, C. Wheeler, M. de Vos, R. Russell, M. Bafadhel","doi":"10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a1721","DOIUrl":"https://doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a1721","url":null,"abstract":"RATIONALE: Reliable remote respiratory monitoring that is acceptable to patients is crucial for healthcare systems and clinical research in the post-COVID-19 era. Existing methods (such as peak-flow, diaries, and pulse oximetry) are limited by adherence and technique or confounded by subjectivity and recall bias. Nocturnal periods provide important signs in respiratory disease activity, yet accurate and unobtrusive methods for home monitoring are lacking. Current gold-standard tools - wearable polysomnography (PSG) devices - capture objective signs such as respiratory rate (RR) but require uncomfortable sensors, which are unsuitable for use beyond a few nights. Emerging monitoring solutions must minimize patient-burden to facilitate long-term engagement in clinical care and research. METHODS: In healthy adults, we evaluated accuracy of a passive, non-contact bedside device (Albus Home RD), that uses wireless motion sensors to capture RR, compared to gold-standard, wearable PSG (SOMNOtouch™ RESPIRATORY, Somnomedics). The table-top Albus Home RD was positioned on the bedside adjacent to the participant in their normal home bedroom environment. Participants slept with usual clothing and bedding;sleeping arrangements ranged from single- to king-size beds with single- and co-sleepers. Gold-standard PSG RR data were recorded using manual count of the raw respiratory traces derived from thoracoabdominal respiratory-effort belts. 10-minute periods from each hour of monitoring were chosen, where sufficient data were available and free from confounding movement and artefacts. Data from Albus Home RD were then analyzed using proprietary signal processing algorithms to output corresponding 30-second RR segments (as breaths/minute). RR results for each device for the selected periods were time-synchronized and compared for each 30-second segment. As per previous validation literature, accuracy was reported as proportion of RR measurements within +/-10% or +/-2 breaths/minute of the PSG RR. RESULTS: 16 healthy adults (9 males, 7 females) participated in overnight monitoring;ages and BMI ranged 20-74 years and 19-38 respectively. Albus Home RD RR measurements for 1540 thirty-second segments were compared against the gold-standard with overall accuracy of 92.4%. Mean Absolute Percentage Error was 0.06 (SD=0.07). CONCLUSIONS: Albus Home RD passively measured RR with 92% accuracy in adults compared to gold-standard in 770 minutes of analysis. Using wireless sensors and proprietary signal processing algorithms, the Albus Home RD is a valid bedside, non-contact monitor of RR in real-world environments. The non-touch, passive nature of this monitor can enable low-burden, long-term home nocturnal monitoring. This system provides new possibilities for remote clinical care and objective data gathering in longitudinal research studies. .","PeriodicalId":159700,"journal":{"name":"TP20. TP020 TELEHEALTH AND REMOTE MONITORING FOR PULMONARY, CRITICAL CARE, AND SLEEP","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127113298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Home Sleep Apnea Testing with Telemedicine in Ostensibly Healthy Adults","authors":"H. Zhongming, C. Cao, T. Penzel, F. Han","doi":"10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a1725","DOIUrl":"https://doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a1725","url":null,"abstract":"","PeriodicalId":159700,"journal":{"name":"TP20. TP020 TELEHEALTH AND REMOTE MONITORING FOR PULMONARY, CRITICAL CARE, AND SLEEP","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122913235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient Satisfaction with Telemedicine in a Virtual Lung Transplant Clinic","authors":"J. Huapaya, J. Kim, A. Varghese, R. Vesselinov, K. Majercak, A. Iacono, B. Griffith, M. Terrin, E. Villalonga-Olives, I. Timofte","doi":"10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a1718","DOIUrl":"https://doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a1718","url":null,"abstract":"Rationale: During the COVID-19 pandemic telemedicine has emerged as an alternative medical care platform, being used more frequently in an effort to decrease the risk of exposure to both the patients and their health care providers. Close monitoring of lung transplant patients is crucial, as they are at a particularly vulnerable population due to chronic immunosuppression and coexisting comorbidities. Our study evaluates the patient satisfaction and technical feasibility of telemedicine visits after lung transplantation. Methods: A retrospective analysis was performed at the University of Maryland Lung Transplant center during the COVID-19 pandemic. The primary outcome was patient satisfaction with the clinic visit measured by a Telemedicine Satisfaction Questionnaire (23 questions). The telemedicine clinics were conducted between March 2020 and November 2020. The survey was designed after a forum discussion with a representative group of lung transplant patients and was initially sent to all lung transplant patients seen in this timeframe;a second follow-up survey was sent six month later. Results: In March 2020, 148 lung transplant patients received an initial survey via email. Fiftythree patients who completed the survey were included in the study. From the survey responses, 94% of patients considered the care they received via the telemedicine program to be very good to excellent;in subsequent follow-up satisfaction was still high at 89% of patients. In the first and second surveys, 96% and 94% of patients reported a good understanding of the use electronic devices, respectively. The majority of patients (59%) reported decreased travel-associated costs during the first survey, which then increased to 85% of patients during the follow-up survey. Conclusion: High levels of patient satisfaction were seen in lung transplant patients after the implementation of a telemedicine program in the context of the COVID-19 pandemic. Decreased travel-associated costs were reported by the majority of patients, especially in the 6-month follow-up survey. Our study suggests that a telemedicine program may decrease the travel-associated costs while maintaining high levels of satisfaction in a very complex population of lung transplant patients. Studies evaluating the role of telemedicine on clinical outcomes require further investigation.","PeriodicalId":159700,"journal":{"name":"TP20. TP020 TELEHEALTH AND REMOTE MONITORING FOR PULMONARY, CRITICAL CARE, AND SLEEP","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115689583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vironix: A Machine-Learned Approach to Remote Screening, Surveillance, and Triage of Viral Respiratory Illness","authors":"S. Swaminathan, B. Toro, N. Wysham, N. Mark, S. Ramanathan, S. Iyer, V. Konda, James Morrill, C. Landon","doi":"10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a1719","DOIUrl":"https://doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a1719","url":null,"abstract":"RATIONALE The Covid-19 pandemic has posed a serious, ongoing global health challenge. The United States has been the worst affected, with more than 11M confirmed cases and 246K deaths (as of November 2020). Two primary and persisting concerns are the continued necessity for shutdown/isolation and the possibility of singular waves of rapid virus spread that could overwhelm global healthcare systems, resulting in preventable mortality and substantial economic burden. While vaccines are being developed and disseminated, the need for remote patient care has never been more critical. To that end, we developed a Covid-19 remote triage software, Vironix, which uses machine-learning algorithms to enable real-time risk stratification and decision support for users. This remote management approach has significant potential to increase safety, improve health outcomes, and stem virus spread as organizations reopen. METHODS Vironix uses personalized machine-learning algorithms trained off clinical characteristic data from the EU, East Asia, and the USA in tandem with prescribed guidelines from the CDC, WHO, and Zhejiang University's handbook on Covid-19 prevention. Clinical characteristics of thousands of patients in the literature were mapped into patient vignettes using Bayesian inference. Subsequent stacked, ensemble decision tree classifiers were trained on these vignettes to classify severity of presenting symptoms and signs. Crucially, the algorithm continuously learns from ongoing use of the application, strengthening decisions, and adapting decision boundaries based on inputted information. Vironix was deployed using a user-friendly API, allowing users to easily screen themselves and obtain remote decision support through a variety of devices (mobile apps, computers, health monitors, etc).RESULTS Algorithm performance was assessed based on its binary classification performance in an out-of-sample test set including severe and nonsevere labels. Vironix correctly assigned the severity classes with an accuracy of 87.6%. Vironix further demonstrated superior specificity (87.8%) and sensitivity (85.5%) in identifying positive (severe) presentations of Covid-19. The algorithms, deployed behind the Vironix Web Application, have been invoked by tens of thousands of users around the world. CONCLUSION 1. The Vironix approach is a highly novel, generalizable methodology for mapping clinical characteristic data into patient scenarios for the purpose of training machine-learning prediction models to detect health deterioration due to viral illness. 2. Vironix exhibits excellent accuracy, sensitivity, and specificity in identifying and triaging clinical presentations of Covid-19 and the most appropriate level of medical urgency. 3. Algorithms continuously learn and improve decision boundaries as individual user input increases. .","PeriodicalId":159700,"journal":{"name":"TP20. TP020 TELEHEALTH AND REMOTE MONITORING FOR PULMONARY, CRITICAL CARE, AND SLEEP","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129896455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}